I'm a PhD candidate in Aerospace Engineering at the University of Cincinnat at the AI BIO Lab focused on fuzzy logic, evolutionary algorithms, AI architecture, and explainable AI. I'm an AI Engineer at Buildings and Builders and founded the Plunk Foundation, building privacy-preserving coordination systems for organizations serving vulnerable families globally.
I build AI systems with transparency, explainability, and privacy by design
- Fuzzy inference systems — Mamdani, TSK, and GA-optimized FIS built from scratch
- Evolutionary algorithms — genetic algorithms for automated system tuning and combinatorial optimization
- Explainable AI — interpretable models for high-stakes domains
- Privacy-preserving ML — fuzzy feature augmentation as a privacy layer under differential privacy
A fuzzy inference system where membership function parameters and rule outputs are evolved by a custom genetic algorithm. Built from scratch in Python. Best training RMSE: 0.0373 after 13 trials of systematic hyperparameter exploration.
Pythonfuzzy logicgenetic algorithmsfrom scratch
Finds the minimum spanning tree for a 9-node offshore pipeline network using a genetic algorithm with Prüfer sequence encoding. Achieves optimal total pipeline length of 41. No graph optimization libraries.
Pythongraph theorycombinatorial optimizationPrüfer sequences
First-order TSK fuzzy inference system trained via gradient descent on hydroelectric power plant data. 25 learned rules, 75 parameters, built entirely from scratch. Final RMSE: 17.46.
Pythonfuzzy logicgradient descentfrom scratch
Full ML pipeline on IRS nonprofit data — regression, classification, and a KMeans hybrid clustering approach. MLP achieves R²=0.82. Built with scikit-learn on a real-world messy dataset.
Pythonscikit-learnregressionclassificationclustering
Fuzzy c-means membership values as noise-resilient features under differential privacy. MLP improves +4.74% RMSE. Presented at NAFIPS 2026. Co-authored with Tri Nguyen and Dr. Kelly Cohen.
Pythonprivacyfuzzy logicNAFIPS 2026published
Mamdani FIS for intelligent prioritization of student peer review feedback across frequency, sentiment, and detail. Reduces HIGH priority inflation by 17%. Presented at NAFIPS 2026.
Pythonfuzzy logicNLPeducationNAFIPS 2026published
Before the PhD I spent 13+ years in industry and founded a student platform company. I turned down a significant acquisition offer rather than build surveillance technology. That decision still anchors how I think about what I build and why.
- University of Cincinnati AI BIO Lab
- Plunk Foundation